Text-Based Healthcare Chatbot Supporting Admitted Patients

Local Chapter Hyderabad, India Chapter

Coordinated byIndia ,

Nikhil Shrestha

Status: Completed

Project Duration: 21 Apr 2022 - 21 May 2022

Open Source resources available from this project

Github Repository

Project background.

As we could imagine the plight of a person (who is undergoing treatment) and related family members.

Initially people don’t get many doubts because they don’t know what’s going to happen. But as time progresses and treatment starts, the concerned person gets anxious, raising various doubts, in mind, regarding post treatment complications and actions required to get to the normal lifestyle. It’s very difficult to find the right person to answer these doubts/questions. For e.g. for diet one has to consult a dietician, for post effects consult a different doctor altogether.

This project is addressing the above issue by creating a chatbot which answers these issues at one place.

The problem.

The objective of this project is first to collect data via the different existing techniques such as web-scraping etc.

The final deliverable includes but is not limited to creating a chatbot that will not only answer the queries/questions etc. but will also let the patient(s) share their experience which helped him/her to overcome post-treatment effects.

The experiences/articles can be read on a web app which will be another deliverable of this project.

Project goals.

Following are (but not limited to) some goals for the projects that defines the problem statement:

- Collect the data from various sources (including hospitals, doctors etc)
- Pre-processing on the collected data
- Building different chatbot (using Rasa framework), sentiment analysis, web-app to host articles and experiences shared.
- Exploratory analysis on data collected using some visualization tools like Tableau, PowerBi etc.

Project plan.

  • Week 1

    • Web scraping

    • Data collection

  • Week 2

    • EDA

    • Data Preprocessing

    • Chatbot modeling

  • Week 3

    • Data Preprocessing

    • Chatbot modeling

    • Chatbot modeling

    • Creating FAQs

    • Tableau/BI visualization tool

  • Week 4

    • Initialization of Web-App

    • Integrating the chatbot

    • Deploying the deliverables.

Learning outcomes.

1. Web-scraping, Analysis of Textual data,
2. NLP including Named Entity Recognition NER
3. Sentiment Analysis, Conversational AI (chatbot using RASA)
4. Visualization/BI Tools
5. Building web-applications.

Demo video

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